Generalized Pairwise Comparisons – GPC
A NEW STATISTICAL METHOD PAVING THE WAY TO PERSONALIZED MEDICINE
The BENEFIT project aims at developing a new method and new tools for the design and analysis of clinical trials, with the objective of involving patients and clinicians in the analysis and place them at the center of the therapeutic decision.
This is achieved through an innovative statistical method, the Generalized Pairwise comparisons (or GPC), that requires to define and prioritize the outcomes related to the study that is considered pertinent regarding patients own situation and preferences.
In doing so, project BENEFIT will provide a novel tool that combines statistical soundness with the inclusion of patient preferences, thereby paving the way to personalized medicine.
For your information, the video uses a real example published in the [New England Journal of Medicine] in 2021 that concluded that antibiotics were noninferior to appendectomy for the treatment of appendicitis.
The Net Treatment BENEFIT: Generalized Pairwise Comparisons (GPC) Method
The method of Generalized Pairwise Comparisons (GPC) fulfills a recent literature trend that attempts addressing a common concern for the analysis of randomized clinical trial data.
While multiple outcomes of interest are typically measured on patients, traditional statistical investigations only concentrate on a ‘’primary’’ outcome. All other outcomes are then either only considered as of secondary importance, or not considered at all in the analysis.
The core idea of GPC is to allow patients and clinicians to elicit among the outcomes of the trial, an order of importance that is most relevant to them. The outcomes can be of any type (categorical, continuous, or time-to-event), and can include efficacy, toxicity, quality of life, or cost-related information.
In conclusion, GPC bridges statistical soundness with clinical relevance by:
1. Incorporating inputs of patients and clinicians into the analysis,
2. Providing a methodologically sound statistical tool, appropriate for the interpretation and communication of the analysis results.
More technical details are available HERE
FOR FURTHER INFORMATON AND BIBLIOGRAPHY FEEL FREE TO CONTACT: Jean-Christophe.Chiem@iddi.com
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Scientific contributions related to GPC
Click HERE to access the full list of communications – papers – book and online chapters
Project funded by: